The utterance-level speaker vector obtained from the pooling layer is fed into a set of four fc layers (Figure REF ). Each of the first two fc layers is followed by a batch normalization [1]} and relu activations. A dense layer is adopted for the third fc layer
and the last fc corresponds to the speaker classification layer. Since ams is used to train the network, the third layer is set up without activation and batch normalization as proposed in [2]}. Once the network is trained, we can extract a speaker embedding from one of the intermediate fc layers. According to [2]}, we consider the second layer as the speaker embedding instead of the third one. The output of this fc layer then corresponds to the speaker representation that will be used for the speaker verification task.
